{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How a Monthly Payment (Equated Monthly Installment) is Calculated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating a Monthly Payment (Simplified)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "662.64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P = 31115 * (1.075)\n",
    "r = 0.0702 / 12\n",
    "n = 60\n",
    "numerator = (r *((1 + r)**(n)) )\n",
    "denominator = ((1 + r)**(n)) - 1\n",
    "emi = P * (numerator / denominator)\n",
    "np.round(emi,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating a Monthly Payment (with some fees included)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "687.23"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P = 31115 + (32615 * 0.0975) + 50 + 200 + 65 + 80\n",
    "r = 0.0702 / 12\n",
    "n = 60\n",
    "numerator = (r *((1 + r)**(n)) )\n",
    "denominator = ((1 + r)**(n)) - 1\n",
    "emi = P * (numerator / denominator)\n",
    "np.round(emi,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The Monthly Payment with fees included is 24.59 higher'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'The Monthly Payment with fees included is {} higher'.format(np.round(687.23 - 662.64,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How Interest Rates/APR Affects Monthly Payments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Total Interest Paid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the steps to do this\n",
    "\n",
    "1-) Divide your interest rate by the number of payments (12) you'll make in the year (interest rates are expressed annually). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "202.93628062500002"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate one month of interest\n",
    "P = 34689.9625\n",
    "r = 0.0702 / 12\n",
    "\n",
    "r * P"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2-) Calculate new principal (after one payment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34205.6725"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "34689.9625 - (687.23 - 202.94)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3-) Repeat steps 1 and 2 using the new principal until the principal reaches 0. You can see can example of this in the Python code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "term = 60\n",
    "P = 34689.96\n",
    "\n",
    "def calc_interest(P,emi,interest_rate = 0.0702):\n",
    "    interest_paid = np.floor(((interest_rate/12)*P)*100)/100\n",
    "    principal_paid = np.round(emi-interest_paid, 2)\n",
    "    new_balance = np.round(P - principal_paid,2)\n",
    "    return(emi, interest_paid, principal_paid, new_balance)\n",
    "\n",
    "payment_list = []\n",
    "for n in range(1, term + 1):\n",
    "    emi,i_paid,p_paid,new_p = calc_interest(P, emi)\n",
    "    payment_list.append([n, P, emi, i_paid, p_paid, new_p])\n",
    "    P = np.round(new_p,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "c_names = ['Month','Starting Balance','Repayment','Interest Paid','Principal Paid','New Balance']\n",
    "payment_table = pd.DataFrame(payment_list, columns = c_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Starting Balance</th>\n",
       "      <th>Repayment</th>\n",
       "      <th>Interest Paid</th>\n",
       "      <th>Principal Paid</th>\n",
       "      <th>New Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>34689.96</td>\n",
       "      <td>687.230218</td>\n",
       "      <td>202.93</td>\n",
       "      <td>484.30</td>\n",
       "      <td>34205.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>34205.66</td>\n",
       "      <td>687.230218</td>\n",
       "      <td>200.10</td>\n",
       "      <td>487.13</td>\n",
       "      <td>33718.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>33718.53</td>\n",
       "      <td>687.230218</td>\n",
       "      <td>197.25</td>\n",
       "      <td>489.98</td>\n",
       "      <td>33228.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>33228.55</td>\n",
       "      <td>687.230218</td>\n",
       "      <td>194.38</td>\n",
       "      <td>492.85</td>\n",
       "      <td>32735.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>32735.70</td>\n",
       "      <td>687.230218</td>\n",
       "      <td>191.50</td>\n",
       "      <td>495.73</td>\n",
       "      <td>32239.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>32239.97</td>\n",
       "      <td>687.230218</td>\n",
       "      <td>188.60</td>\n",
       "      <td>498.63</td>\n",
       "      <td>31741.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>31741.34</td>\n",
       "      <td>687.230218</td>\n",
       "      <td>185.68</td>\n",
       "      <td>501.55</td>\n",
       "      <td>31239.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>31239.79</td>\n",
       "      <td>687.230218</td>\n",
       "      <td>182.75</td>\n",
       "      <td>504.48</td>\n",
       "      <td>30735.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>30735.31</td>\n",
       "      <td>687.230218</td>\n",
       "      <td>179.80</td>\n",
       "      <td>507.43</td>\n",
       "      <td>30227.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>30227.88</td>\n",
       "      <td>687.230218</td>\n",
       "      <td>176.83</td>\n",
       "      <td>510.40</td>\n",
       "      <td>29717.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  Starting Balance   Repayment  Interest Paid  Principal Paid  \\\n",
       "0      1          34689.96  687.230218         202.93          484.30   \n",
       "1      2          34205.66  687.230218         200.10          487.13   \n",
       "2      3          33718.53  687.230218         197.25          489.98   \n",
       "3      4          33228.55  687.230218         194.38          492.85   \n",
       "4      5          32735.70  687.230218         191.50          495.73   \n",
       "5      6          32239.97  687.230218         188.60          498.63   \n",
       "6      7          31741.34  687.230218         185.68          501.55   \n",
       "7      8          31239.79  687.230218         182.75          504.48   \n",
       "8      9          30735.31  687.230218         179.80          507.43   \n",
       "9     10          30227.88  687.230218         176.83          510.40   \n",
       "\n",
       "   New Balance  \n",
       "0     34205.66  \n",
       "1     33718.53  \n",
       "2     33228.55  \n",
       "3     32735.70  \n",
       "4     32239.97  \n",
       "5     31741.34  \n",
       "6     31239.79  \n",
       "7     30735.31  \n",
       "8     30227.88  \n",
       "9     29717.48  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payment_table.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6543.51"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(payment_table['Interest Paid'].sum(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loan and Principal Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://stackoverflow.com/questions/21918718/how-to-label-certain-x-values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 1, figsize=(10, 5) )\n",
    "axes.plot(payment_table['Month'], payment_table['Principal Paid'], c = 'b', label = 'Principal');\n",
    "axes.plot(payment_table['Month'], payment_table['Interest Paid'], c = 'k', label = 'Interest');\n",
    "\n",
    "axes.set_xlim((1, 60));\n",
    "axes.set_xticks([1, 10, 20, 30, 40, 50, 60])\n",
    "axes.set_ylim((0, 700));\n",
    "axes.set_ylabel('Dollars', fontsize = 22);\n",
    "axes.set_xlabel('Month', fontsize = 22);\n",
    "\n",
    "plt.xticks(fontsize = 20)\n",
    "plt.yticks(fontsize = 20)\n",
    "axes.set_title('Interest and Principal Paid Each Month', fontsize = 24)\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.02,0), loc=\"lower left\", borderaxespad=0, fontsize = 20)\n",
    "plt.tight_layout()\n",
    "plt.grid(axis = 'both')\n",
    "plt.savefig('Interest_Principal.png', dpi = 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Refinancing Cost Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.59% vs 7.02% (show the cost of refinancing a car, assuming no prepayment penalty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "P = 34689.96\n",
    "term = 60\n",
    "\n",
    "def generate_loan_table(P, term, interest_rate=0.0702):\n",
    "\n",
    "    def calc_emi(P, n, interest_rate):\n",
    "        r = interest_rate / 12\n",
    "        numerator = (r *((1 + r)**(n)) )\n",
    "        denominator = ((1 + r)**(n)) - 1\n",
    "        emi = P * (numerator / denominator)\n",
    "        emi = np.round(emi, 2)\n",
    "        return(emi)\n",
    "    \n",
    "    def calc_interest(P, emi, interest_rate):\n",
    "        # Assuming no fractional interst on a loan \n",
    "        interest_paid = np.floor( ((interest_rate / 12) * P ) * 100) / 100\n",
    "        principal_paid = np.round(emi - interest_paid, 2)\n",
    "        new_balance = np.round(P - principal_paid,2)\n",
    "        return(emi, interest_paid, principal_paid, new_balance)\n",
    "\n",
    "    emi = calc_emi(P, term, interest_rate)\n",
    "    payment_list = []\n",
    "    \n",
    "    for n in range(1, term + 1):\n",
    "        emi,i_paid,p_paid, new_p = calc_interest(P, emi, interest_rate)\n",
    "        payment_list.append([n, P, emi, i_paid, p_paid, new_p])\n",
    "        P = np.round(new_balance,2)\n",
    "   \n",
    "    payment_table = pd.DataFrame(payment_list, columns = ['Month',\n",
    "                                                       'Starting Balance',\n",
    "                                                       'Repayment',\n",
    "                                                       'Interest Paid',\n",
    "                                                       'Principal Paid',\n",
    "                                                       'New Balance'])\n",
    "    return(payment_table, np.round(payment_table['Interest Paid'].sum(), 2), emi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "original_table, original_paid, original_emi = generate_loan_table(P, term, interest_rate = 0.0702)\n",
    "refinanced_table, refinanced_paid, refinanced_emi = generate_loan_table(P, term, interest_rate = 0.0359)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6543.51, 3257.88)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_paid, refinanced_paid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Refinancing could save: 3285.63'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Refinancing could save: {}\".format(6543.51 - 3257.88)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "687.23"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_emi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "632.47"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refinanced_emi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54.76"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(original_emi - refinanced_emi, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total Interest Through Different Loan Terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "original_table, original_paid, original_emi = generate_loan_table(P, term = 60, interest_rate = 0.0702)\n",
    "seventyTwo_table, seventyTwo_paid, seventyTwo_emi = generate_loan_table(P, term = 72, interest_rate = 0.0702)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6543.51, 7916.58, 1373.07)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_paid, refinanced_paid, np.round(refinanced_paid - original_paid, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(687.23, 591.76)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_emi, seventyTwo_emi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 1, figsize=(10, 5) )\n",
    "axes.plot(seventyTwo_table['Month'], seventyTwo_table['Interest Paid'].cumsum(), c = 'k', marker = '.', markersize = 10, label = '72 Month Term Loan');\n",
    "axes.plot(original_table['Month'], original_table['Interest Paid'].cumsum(), c = 'b', marker = '.', markersize = 10, label = '60 Month Term Loan');\n",
    "\n",
    "axes.set_xlim((1, 72));\n",
    "axes.set_xticks([1, 12, 24, 36, 48, 60, 72])\n",
    "axes.set_ylim((0, 9000));\n",
    "axes.set_ylabel('Dollars', fontsize = 24);\n",
    "axes.set_xlabel('Month', fontsize = 24);\n",
    "\n",
    "plt.xticks(fontsize = 22)\n",
    "plt.yticks(fontsize = 22)\n",
    "axes.set_title('Total Interest Paid (7.02% Interest)', fontsize = 26)\n",
    "\n",
    "plt.legend(loc=\"lower right\", fontsize = 20)\n",
    "plt.tight_layout()\n",
    "plt.grid(axis = 'both')\n",
    "plt.savefig('Total_Interest_Paid.png', dpi = 1000)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:py37]",
   "language": "python",
   "name": "conda-env-py37-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
